Unsupervised machine learning for identification of audience subpopulations and dimensionality and/or sparseness reduction techniques to facilitate identification of audience subpopulations

ABSTRACT

Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional of and claims priority to U.S. Provisional Patent Application No. 62/737,620, filed Sep. 27, 2018, the disclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND

Some embodiments described herein relate to unsupervised machine learning techniques that enable improvements in identifying target audiences and techniques for reducing the sparseness of data sets to enable otherwise unfeasible, unsupervised machine learning.

Typically, brands have employed market research firms to conduct surveys and/or focus groups to better understand their customers. Known forms of market research, however, can take months to complete and cost millions of dollars. Moreover, survey question selection can have a large impact on the results of a market research survey. A market research firm may be unable to formulate questions that would identify or characterize an audience the market research firm does not know the brand has. For example, if a brand is primarily focused on stay-at-home moms, a market research survey may be unable to identify that the brand also has a significant following among outdoorsmen and/or may be unable to develop insights into what outdoorsmen's interests in the brand might be. Similarly, focus group-based market research can also be biased (unintentionally or otherwise) because it may be difficult or impossible to identify a representative set of subjects that accurately portrays a company's overall and/or target customer set. Known forms of market research are also limited by relying on self-reported behavioral data, which relies on the subjects' honesty, memory, and introspective abilities.

New techniques for characterizing audiences and selecting targets for the delivery of targeted content have arisen in the internet age, and targeted content delivery has become a fundamental feature of the modern internet. Targeted content delivery can be roughly divided into two distinct modes, retargeting and prospecting. Retargeting involves providing targeted content to people who have previously taken a predefined action, while prospecting involves predicting which people are likely to be interested in targeted content. For example, during a retargeting campaign, individuals who have previously visited particular predefined webpages, purchased certain predefined items, and/or have social media connections with predefined profiles, may be selected to receive targeted content. Retargeting can include sending brand-related content to individuals who have previously interacted with the brand. Prospecting, by contrast, seeks to identify individuals who may be interested in the targeted content who have not been observed taking any particular predefined action. Prospecting generally involves the analysis of a relatively large amount of data associated with the user.

Modern prospecting is typically a “big data” operation, in which sophisticated algorithms process large amounts of information with the goal of quickly grouping or classifying individuals based on their predicted affinity to a content item. Known techniques, however, suffer from a number of drawbacks. For example, some known supervised learning techniques seek to predict how likely an individual is to perform a particular action (e.g., buy a product, click an advertisement, etc.). Before a supervised learning technique can be applied, a model must be trained using data that includes a measure of the action sought to be predicted. Selecting the action to be predicted, however, can be a significant challenge and/or data revealing the occurrence of the predicted action may be unavailable. For example, a brand selling sports equipment may be interested to know that it has a significant following among parents, and to identify those customers of its products that are parents to target them or to learn more about them. A supervised algorithm would not be able to identify this group of users from the full customer base unless (a) the brand already knew there was a group of parents in its customer base and (b) there were available examples of which users were parents. Without a very specific data source that labels this exact set of people, supervised learning techniques would be unable to identify that the brand has a significant following among parents, much less identify specific individuals as parents. Moreover, due to deficiencies of supervised learning techniques, the brand would generally be unable to identify suitable audiences if the suitable audience is not identified in the data revealing the occurrence of the predicted action. Similarly stated, even if the brand selling sports equipment were able to identify parents, many other subpopulations exist for which traditional market research data is not coded, particularly characteristics the brand has not previously identified as relevant.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system that includes an audience identification device, according to an embodiment.

FIG. 2 is a flow chart of a method of identifying subpopulations of an audience, according to an embodiment.

FIG. 3 is an example of a taxonomic map of an audience of a technology website, produced according to the method of FIG. 2 .

FIG. 4 is a pie chart showing the size of sub-subpopulations identified in FIG. 3 .

FIG. 5 is a flow chart of a method of tracking subpopulations (and/or lower ranked taxonomical orders), according to an embodiment.

FIG. 6 is a flow chart of a method of tracking subpopulations, according to an embodiment.

FIG. 7 is a visualization of the internet generated by a technique that includes embedding websites in a p-dimensional space, according to an embodiment.

FIG. 8 is a flow chart of a method for clustering and/or mapping internet, according to an embodiment.

FIG. 9 is a flow chart of a method for generating a conversion likelihood, according to an embodiment.

DETAILED DESCRIPTION

Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

Some embodiments described herein relate to a computer-implemented method that includes accessing sparse behavioral vectors. Each sparse behavioral vector can be associated with a user. Each element of each sparse behavioral vector represents a different detectable behavior. Multiple supervised machine learning models can be applied to each sparse behavioral vector. Each supervised learning model can be uniquely associated with a potential detectable behavior and configured to produce a score representing a probability that a user will perform that potential detectable behavior. The scores produced by the supervised machine learning can be used to define dense behavioral vectors; each element of each dense behavioral vector can correspond to a score produced by a different supervised machine learning model applied to the corresponding sparse behavioral vector. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, of dense behavioral vectors based on similarities between them. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

Some embodiments described herein relate to a computer-implemented method that includes receiving website visitation data. A machine learning technique can be applied to define associations between a plurality of websites represented in the website visitation data. An embedding of the plurality of websites in p-dimensional space can be defined based on the associations between the plurality of websites. A plurality of clusters of websites can be identified based on proximity of websites from the plurality of websites to each other in the p-dimensional space. A position of a user in the p-dimensional space can be identified based on website visitation data for the user. Delivery of targeted content to the user can be facilitated based on the user's position to relative to a cluster of websites. For example, targeted content can be selected based on the cluster nearest to the user in the p-dimensional space.

Some embodiments described herein relates to a computer-implemented method that includes accessing website visitation records for a first group of user devices. A p-dimensional embedding of websites can be generated based on the website visitation records for the first group of user devices. Conversion event data associated with website visitations records for a second group of user devices can be accessed. A position of each user device from the second group of user devices in the p-dimensional embedding can be determined based on the website visitation data for the second group user devices. Using the conversion event data and the website visitation records for the second group of user devices, a machine learning model can be trained to predict whether a user device is likely to take a conversion action, such as purchasing a good or service based on the position of that user device in the p-dimensional embedding. An indication that a user device that is not from the first group of user devices or the second group of user devices can be received. A position of that user device in the p-dimensional embedding can be determine. Delivery of targeted content to that user device can be facilitated based on predicting whether that user device is likely to take a conversion action based on the position of that user device in the p-dimensional embedding.

In some embodiments, a method includes receiving multiple website traffic data records, the multiple website traffic data records is associated with multiple user devices. The method further includes generating a website traffic embedding based on the multiple website traffic data records. The method further includes defining training data including the website traffic embedding and website traffic data records for at least one of the multiple user devices and multiple conversion event data associated with at least one of the multiple user devices. The training data can be used to train a machine learning model. An indication of advertisement display opportunity can be provided to the machine learning model based on a user device that is not from the multiple user devices accessing a webpage. The machine learning model can predict whether that user device is likely to undertake a conversion action.

Some embodiments described herein relate to unsupervised learning techniques, and in some more specific embodiments, clustering, and/or agglomerative hierarchical clustering. Unlike supervised learning, such techniques do not require training data that includes a measure of a desired outcome. Some embodiments described herein apply unsupervised learning techniques to user web visitation data. Web visitation data is a sparse, high-dimensional data set. In this sparse, high-dimensional space, no natural way exists to define a meaningful distance between any two users. For example, two users with no website history in common will have a similarity of zero. Because the space is so sparse, many pairwise similarities are zero, which means clustering in this space is impractical or gives results with limited usefulness. Some embodiments described herein relate to specific novel computational techniques for reducing the dimensionality and sparseness of sparse data sets, such as web visitation data, which can allow computers to apply unsupervised modeling techniques to large, sparse data sets that have been densified.

Raeder, T. et al., Scalable Supervised Dimensionality Reduction Using Clustering, Proceedings of the 19^(th) ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2013) (“Raeder”), the disclosure of which is hereby incorporated by reference in its entirety, describes a technique related to those described in the present application. Techniques described in Raeder involve reducing dimensionality by performing clustering on websites (or representations of websites) themselves, so that fewer dimensions can be used to represent the space of all websites. The technique described in Raeder, however, does not address the sparseness problem or reduce the dimensionality of users (or representations of users). Rather, Raeder collapses the existing non-zero dimensions that represent attributes of websites into fewer non-zero dimensions, but does not introduce new non-zero dimensions to describe each website. The resulting space generated by applying techniques described in Reader may have lower-dimensionality, but still very sparse, so that pairwise similarities between users, described by their history of website visitations, are zero for many users. Second, because Raeder's solution reduces dimensionality by representing many different websites with the same feature, information about the differences between those websites is lost, so that two users who have different histories among those websites have the same representation in the low-dimensionality space and thus have a pairwise distance of zero. To meaningfully group together users with different characteristics with useful options for the number and size of clusters, a need remains for a technique that captures a range of meaningful pairwise distances between users.

FIG. 1 is a schematic illustration of a system 100, according to an embodiment. The system 100 includes an audience identification device 110, a targeted content provider 120, one or more webservers 130, and one or more user devices 160, each communicatively coupled via a network 190. The network 190 can be the internet, an intranet, a local area network (LAN), a wide area network (WAN), a virtual network, a telecommunications network, any other suitable communication system and/or combination of such networks. The network 190 can be implemented as a wired and/or wireless network.

The user devices 160 are computing entities, such as personal computers, laptops, tablets, smartphones, or the like, each having a processor 162 and a memory 164. The processor 162 can be, for example, a general purpose processor, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), and/or the like. The processor 162 can be configured to retrieve data from and/or write data to memory, e.g., the memory 164, which can be, for example, random access memory (RAM), memory buffers, hard drives, databases, erasable programmable read only memory (EPROMs), electrically erasable programmable read only memory (EEPROMs), read only memory (ROM), flash memory, hard disks, floppy disks, cloud storage, and/or so forth. Each user device 160 can be operable to access one or more of the webservers 130. For example, a user operating a user device 160 to browse the internet (e.g., the network 190) can access webpages stored on one or more of the webservers 130. The webservers 130 can be computing entities each having a processor 132 and a memory 134, which can be structurally and/or functionally similar to the processors 162 and/or 164, respectively, discussed above.

The targeted content provider 120 can be a computing entity operable to select, deliver, and/or facilitate the delivery of one or more items of targeted content. For example, the targeted content provider 120 can be associated with an advertiser or advertising network that provides targeted content that is displayed by a user device 160 when that user device 160 accesses a particular webserver 130. Similarly stated, targeted content selected, delivered, or facilitated by the targeted content provider 120 can include advertisements embedded within, displayed with, or otherwise associated with webpages displayed by a user device 160. The targeted content provider 120 includes a processor 122 and a memory 124, which can be structurally and/or functionally similar to the processor 162 and/or memory 164, respectively, discussed above.

The audience identification device 110 can be a computing entity configured to receive signals indicative of actions or behaviors of users associated with the user devices 160. For example, the audience identification device 110 can receive web visitation data for each of the user devices 160 and/or webservers 130 using cookie-based or any other suitable technique for network traffic attribution (e.g., any suitable technique for identifying that a user device was used to access a webserver including, for example monitoring Internet Protocol (IP) addresses of user devices 160, user agents of user devices 160 and/or browser fingerprints). In addition or alternatively, any other suitable signal, such as a signal representing behavioral data can be received by the audience identification device 110 and associated with a user of one or more of the user devices 160. For example, the audience identification device 110 can receive conversion event data. Conversion event data can include, for example, purchase information from purchase confirmation websites, purchase history associated with a user account, a credit reporting bureau, customer loyalty program, survey information, or any other suitable source. Conversion event data can also include information regarding whether a user took any suitable brand action, such as clicking on a predefined advertisement or like, visiting a predefined website, physically visiting a retail location, or any other suitable action. The audience identification device 110 includes a processor 112 and a memory 114, which can be structurally and/or functionally similar to the processor 162 and/or the memory 164, respectively, discussed above.

As discussed in further detail herein, the audience identification device 110 can be operable to apply machine learning techniques to identify subsets of the user devices 160 or users associated with one or more user devices 160 (also referred to herein as subpopulations) based on the web visitation or other behavioral data. In some embodiments, subpopulations are identified based on a predicted affinity towards the targeted content, and not based on users within the subpopulation having taken one or more predefined actions. Similarly stated, the audience identification device 110 can be suitable to perform prospecting.

Machine learning techniques performed by the audience identification device 110 can be used to identify one or more subpopulations. The audience identification device 110 can be operable to send a signal to the targeted content provider 120 and/or other suitable party or device that includes a representation of the one or more subpopulations. The subpopulations can be studied to identify particular subpopulations that the targeted content provider 120 desires to reach or study. Unless specified otherwise, references to unsupervised machine learning and/or clustering refer specifically to hierarchical clustering. It should be understood, however, that techniques described in the context of clustering may be applicable to other suitable machine learning techniques, such as k-means clustering, and/or unsupervised co-clustering. Similarly stated, the audience identification device 110 can be operable to break an audience into subpopulations. In some instances, the audience identification device 110 can further be operable to predict which subpopulations are receptive to and/or likely to convert an item of targeted content via any suitable unsupervised machine learning technique. In addition or alternatively, the audience identification device 110 can be operable to prepare and/or transmit analytics and/or other suitable reports that can aid a marketer or other suitable entity to understand a brand's audience, including identifying and reporting on the one or more subpopulations.

In some embodiments, the memory 114 can store a vector for each user device 160 and/or for each user associated with one or more user devices 160. Such a vector can represent that user device's/user's behavior in a format discussed in further detail below. Using techniques described in further detail below, the processor 112 can be operable to perform machine learning techniques on a matrix comprising vectors of an audience (set of users) to identify subpopulations. Subpopulations can further be clustered to identify sub-subpopulations. In some embodiments the number subpopulations within each taxonomic rank and/or the number of taxonomic ranks can be user-selectable.

A vector for a user/user device 160 can include a large number of elements. Each element can represent a different webserver 130, a different uniform resource locator (URL), or any other suitable indicator of a potentially detectable behavior. If a user/user device 160 has visited a URL/webserver 130 or otherwise engaged in a behavior associated with a particular vector element, the element for that behavior and/or URL/webserver 130 in that user's vector can be set to 1. Conversely, if a user/user device 160 has not visited a URL/webserver 130 or otherwise engaged in a behavior associated with a particular element, the element for that behavior and/or URL/webserver 130 in that user's vector can be a 0. Alternatively, non-binary schemes are also possible. For example, each element can be a representation of that user's engagement with a URL/webserver 130 (e.g., representing number of times visited, time spent, links clicked, etc.). Similarly stated, each vector can be a representation of a user's/user device's 160 web browsing history. Given the size of the internet (e.g., the network 190) and/or universe of possible tracked behaviors, such a vector would be large and sparse, with, on average, less than 0.1%, less than 0.01%, or less than 0.001% of entries having a non-zero value. The processor 112 can be operable to identify subpopulations, sub-subpopulations and so forth by performing clustering on a set of vectors (a matrix) representing an audience (e.g., the set of users/user devices 160).

FIG. 2 is a flow chart of a method of identifying subpopulations of an audience, according to an embodiment. At 200, the audience can be defined. The audience can be, for example, all users or a subset of users for whom behavioral data is available, visitors to a particular webpage(s) (e.g., webserver(s) and/or URL(s)), customers who bought a particular product(s) or services(s) (e.g., on the internet and/or as identified from a customer loyalty program), people who have visited a particular physical location, people who have expressed interest in a webpage as determined by a classification model, and/or any other suitable audience identified through any suitable means. At 210, behavioral data for members of the audience can be received. For example, behavioral data for a subset of users for whom behavioral data is available can be received. As discussed above with reference to FIG. 1 , the behavioral data can be web history or any other suitable behavior data received at an audience identification device. Similarly stated, in some embodiments, the behavioral data can be based on direct and/or passive observation, not self-reported (e.g., survey) data. In other embodiments, behavioral data can be based on and/or include survey data.

At 220, a sparse behavior vector for each member of the population can be defined based on the behavioral data received at 210. The sparse behavior vector can include an element for each potential detectable behavior. For example, each element of the sparse behavior vector can represent a different website/URL, product available for purchase, and/or the like. Given the number of websites/URLS, products available for purchase, and other identifiable behaviors, the sparse behavioral vector can be tens of thousands, hundreds of thousands, millions, tens of millions, hundreds of millions, billions, or tens of billions of elements long. The vast majority (99.9%, 99.99%, 99.999% or more) of the sparse behavioral vector for each audience member can be zeros or otherwise indicate that the member of the audience (e.g., a user device and/or user associated with a user device) represented by that sparse behavioral vector has not been observed performing a behavior associated with that element. A small number of elements (e.g., 0.1%, 0.01%, 0.001% or fewer) for each audience member can be ones or otherwise indicate that the member of the audience represented by that sparse behavioral vector performed a behavior associated with that element. Many unsupervised learning techniques require a measure of similarity or distance between pairwise combinations of records. When the behavioral vectors are extremely sparse, most pairs of users have no overlapping dimensions with non-zero values. This leads to a situation where, depending on the choice of distance metric, many pairwise distances are either not meaningful, or take the maximum distance value (e.g., a similarity of zero), so this sparse representation does not contain enough information to achieve meaningful results using these unsupervised learning techniques.

At 230, m supervised learning models can be applied to each sparse behavioral vector defined at 220. Five hundred is an example of a suitable m, and, for ease of description, references herein to five hundred should be understood as referring to m. It should be understood, however, that m can be any other suitable integer greater than 2, such as 50, 100, 1000, or 5000. For example, a model for each of 500 websites or URLs represented by an element in the sparse behavioral vectors can be defined. Supervised models are used here because each of the 500 models can produce an output or score for each sparse behavioral vector. The score can represent, for example, a prediction of the likelihood of a user performing a potential detectable behavior (e.g., visiting a webpage, purchasing a good/service, etc.), an affinity of a user/user device associated with that sparse behavioral with the selected website or URL, a prediction or probability that a user/user device will make a purchase, and/or a prediction or probability that a user/user device will perform a conversion action. For example, the score can be an integer or floating point value in which higher values represent strong associations with the selected URL and/or products/services associated with the URL. For example, one of the 500 models can predict a likelihood that a user represented by a sparse behavioral vector will visit espn.com, while another of the 500 models predicts a likelihood of a user visiting bbc.co.uk, while another of the 500 models will predict the likelihood of a user visiting etsy.com. The 500 websites or URLs can be selected randomly or manually as representatives of categories of websites. Notably, the 500 websites or URLs may be selected independently of, be unrelated to, and/or be entirely different from the audience identified at 200. Similarly stated, the supervised learning models applied, at 230, do not seek to identify, characterize, or segment the audience. For example, the audience identified at 200 might be users who have been observed visiting budwiser.com, while the 500 models may not include budwiser.com and/or may be selected independently of identifying the audience as visitors of budwiser.com, at 200. In this way, the 500 supervised learning models applied at 230 produce 500 partially, substantially, or completely independent measures of the sparse vector. Thus, in some instances, one or more of the 500 models may seek to predict an affinity of the audience for a website or other potential behavior that no audience members have been detected visiting/performing.

In some embodiments, the 500 models can be trained using a data set that is distinct from data representing the audience. In other embodiments at least portion of the data representing the audience can be used to train the 500 models, while another at least a portion of the data representing the audience can be used to validate the 500 models.

At 240, a dense behavioral vector can be defined based on the outputs of the 500 models. For example, a dense behavioral vector having a length of 500 can be defined for each sparse behavioral vector where each element of the dense behavioral vector is a score produced by one of the 500 models, placing each dense behavioral vector in an m-dimensional space. Optionally, at 245, a distance from each dense behavioral vector can be measured from each other dense behavioral vector using a suitable distance metric like cosine distance, correlation distance, or Euclidean distance, placing each dense behavioral vector in n-dimensional space, where n is the size of the audience (e.g., the number of dense behavioral vectors and users/user devices).

At 250, unsupervised machine learning techniques can be applied to the dense behavioral vectors, in m-dimensional space or optionally as they exist in the n-dimensional space, to define a number of subpopulations. As discussed above, the densification of sparse behavioral vectors into m-dimensional (or n-dimensional) space can facilitate unsupervised learning techniques that could not otherwise be applied to the sparse behavioral vectors. Such unsupervised learning techniques can provide insights not previously available via supervised techniques, such as the identification of unexpected subpopulations. Similarly stated, applying the techniques described herein allow a compute device to perform analyses that would previously been impossible.

In some embodiments, the number of subpopulations, k, the audience is clustered into can be a user-definable parameter. Similarly stated, an analyst associated with an audience identification device can specify the number of clusters (subpopulations) to be produced. Optionally, at 255, each subpopulation can be further clustered into lower ranked taxonomic orders using a similar unsupervised machine learning technique or any other suitable clustering technique. If the original unsupervised machine learning technique was a hierarchical method, a further clustering can be performed based on the original hierarchical results. The clustering process can be repeated any number of times to produce finer and finer-grained subpopulations. Each subpopulation and/or lower taxonomical ranks can be characterized by any suitable technique, including, for example, by characteristics based on web history, media usage, or other indications of subpopulation interests. Characterizing subpopulations can include, for example, the top website visited by that subpopulation, optionally normalized against generic internet traffic. Subpopulations and/or sub-subpopulations can be used to identify users for the delivery of targeted content.

FIG. 3 is an example of a taxonomic map of an audience of a technology website, produced according to the method of FIG. 2 . Similarly stated, the taxonomic map depicted in FIG. 3 can be an output of clustering dense behavioral vectors at 250. FIG. 3 illustrates the audience 300 of the technology website. The audience 300 is subdivided into a sixteen subpopulations (numbered 1-16) according to the method described above. Higher taxonomical orders (i.e., combinations of related subpopulations) are further identified. Each of the subpopulations is further divided into between two (subpopulation 3) and fifteen (subpopulation 14) sub-subpopulations. Although not shown, each sub-subpopulation could be further divided into lower taxonomical orders. The y-axis of FIG. 3 represents the distance between two groups in m-dimensional space, as discussed above with reference to FIG. 2 and event 245.

FIG. 4 is a pie chart showing the size of subpopulations identified in FIG. 3 . Because the machine learning technique applied to produce subpopulations, sub-subpopulations, and lower ranked taxonomic orders, at 250 and 255, is unsupervised, the output of the clustering technique does not depend on an analyst pre-identifying target groups or particular propensities. Rather, each subpopulation (and lower ranked taxonomical orders) are defined organically, which can be used to obtain new insights and new targetable audiences for a content provider interested in sending targeted content. Such insights could also be used by a brand manager, marketing strategist, or product designer. Similarly stated, the unsupervised nature of the technique used to identify the subpopulations does not involve an analyst specifying attributes, the identification of seeds or audience members around which a subpopulation is assembled, or otherwise pre-identifying features characteristic of a subpopulation prior to the application of the unsupervised machine learning technique. For example, although subpopulations having an interest in consumer technology 14 may be expected in an audience visiting a technology website, relatively large sub-subpopulations having interests in car parts and trucks 5, celebrities, children 11, and/or Cincinnati 13 may not be selected for analysis by supervised learning techniques. Similarly stated, were the audience of the technology website modeled using supervised learning techniques, the analyst responsible for training the model may not have the foresight to select training data suitable to detect sub-subpopulations with interest in car parts, trucks, celebrities, children, Cincinnati, and so forth. Identifying such hidden and/or unexpected niche audiences can allow for more precise targeting of content, planning of brand strategy, or design of future products or features. For example, a content provider interested in sending targeted content may select content prepared for the Cincinnati Convention & Visitors Bureau to accompany the technology website, where such content may reach a surprisingly large and/or receptive audience.

FIG. 5 is a flow chart of a method of tracking subpopulations (and/or lower ranked taxonomical orders), according to an embodiment. At 260 and during a first time period, subpopulations of an audience can be identified. The subpopulations (and/or lower ranked taxonomical orders) can be identified via the method shown and described above with reference to FIG. 2 . At 270 of FIG. 5 , the method can be repeated for a second time period and/or a second audience. For example, the method described with reference to FIG. 2 can be performed for an audience of a website over a first time period, and then again for a second audience of the same website over a second time period. The first time period and the second time period may overlap or be mutually exclusive. As discussed in further detail below, at 280, the subpopulations identified during the first time period at 260 can be compared to the subpopulations identified during the second time period at 270. Changes in the size of one or more subpopulations, disappearance of one or more subpopulations, and/or new subpopulations can be identified, at 280. Tracking subpopulations according to the method depicted in FIG. 5 can be performed automatically and for any number of audiences. For example, the audience identification device described above with reference to FIG. 1 can be operable to track multiple audiences simultaneously (e.g., an audience for each of several websites) to identify subpopulations for each of several audiences (e.g., according to the method depicted in FIG. 2 ), and to identify changes in subpopulations. When a change of size of a subpopulation exceeds a threshold, when a new subpopulation emerges/is identified, and/or when a subpopulation disappears, the audience identification device can send an alert, for example to a content provider interested in sending targeted content, analyst, brand manager, and/or so forth which can in turn modify a strategy for the delivery of targeted content, marketing plan, or product development plan, etc. In some such embodiments, a targeted content provider or other entity identified in changes in subpopulations can send content to one or more subpopulations based on the identification of a new subpopulation. For example, a new subpopulation or growth of a subpopulation may represent a type of consumer with different interests than a previous type of consumer and/or an increasing importance of a new type of consumer, and the marketer may want to develop new messaging to reach this type of consumer. A decrease in size in a particularly valuable subpopulation (as measured by layering on some other data such as conversion rate or cart size) may indicate that the current marketing or targeting strategy is unsuccessful and should be changed. Although the subpopulations defined at 260 and 270 can be associated with any suitable time periods and/or differ by any suitable time, typically, the subpopulations defined at 260 and 270 represent audiences observed over a period of hours to months and differ by days to months.

At 280, a mapping between the subpopulations identified at 260 to the subpopulations defined at 270 can be defined. Because the clustering is unsupervised, there is no expectation that any particular subpopulation (e.g., subpopulation 1) identified at 260 is the same as a similar subpopulation identified at 270. Similarly, there is no pre-defined identification of which subpopulations are new or which subpopulations have disappeared. At 280, therefore, a mapping between subpopulations can be defined in one of several ways. For ease of description, the subpopulations identified at 260 will be referred to collectively as taxonomy A, and a subpopulations identified at 270 will be referred to collectively as taxonomy B.

According to one embodiment, a supervised multi-class classification (or model) can be trained on taxonomy A. This model predicts to which of the subpopulations in taxonomy A a user belongs. The model trained on taxonomy A can be applied to taxonomy B, such that each user in each subpopulation in taxonomy B has a label corresponding to a subpopulation in taxonomy A or a label that indicating no corresponding subpopulation in taxonomy A was identified. In this way, at 280, each subpopulation in taxonomy B can be mapped to a similar subpopulation in taxonomy A (or vice versa), or to introduce a new subpopulation(s) if there is no clear mapping to a subpopulation in taxonomy A. In some instances, a reverse mapping can identify users as belonging to corresponding subpopulations in taxonomy A and taxonomy B if a pre-defined level of agreement between a forward mapping and a reverse mapping is exceeded. Similarly stated, first taxonomy A can be mapped to taxonomy B, followed by reverse mapping taxonomy B to taxonomy A; a user can be identified as belonging to associated subpopulations in taxonomies A and B if the two mappings agree. This process can be extended for each user in taxonomy A and taxonomy B.

According to another embodiment, at 280, a sample of users from taxonomy A can be added to taxonomy B before performing the unsupervised learning on taxonomy B. In this way, each subpopulation in taxonomy B has a sample of users from taxonomy A that can be used to establish a mapping between taxonomies. If there are no members of a particular subpopulation in taxonomy A in a subpopulation in taxonomy B, or there is not sufficient agreement between subpopulations of users in taxonomy A and taxonomy B, then a new subpopulation can be defined in taxonomy B.

FIG. 6 is a flow chart of a method of tracking subpopulations, according to an embodiment. At 360, subpopulations of a first audience can be identified. The first audience can be, for example, visitors to a webpage. The first subpopulations (and/or lower ranked taxonomical orders) can be identified via the method shown and described above with reference to FIG. 2 . At 372, subpopulations of a second audience can be identified. The second audience can be a subset of the first audience. For example, the second audience can be users/user devices who became members of the first audience after receipt of an item of targeted content, such as an advertisement. At 374, subpopulations of a third audience can be identified. The third audience can be a subset of the first audience. For example, the third audience can be users/user devices who became members of the first audience based on a referral from another media channel, such as from a search result page, or a radio, billboard, or television advertisement. At 380, the subpopulations of the first audience can be compared the subpopulations of the second audience and/or the subpopulations of the third audience using techniques similar to those described above with reference comparing subpopulations at 280. The comparison, at 380, can allow the audience identification device and/or the targeted content provider to understand whether targeted content is effective, who targeted content reaches, and/or how the audience differs depending on how audience members reached the webpage. In some embodiments, the audience identification device, targeted content provider, or other entity can send targeted content or take any other suitable action based on the comparison at 380.

FIG. 7 is a visualization of the internet generated by a technique that includes embedding websites in a p-dimensional space, according to an embodiment. FIG. 8 is a flow chart of a method for clustering and/or mapping internet locations (also referred to as URLs or websites), according to an embodiment. Clustering and/or mapping websites according to methods described herein can serve to identify similarities between websites that are not otherwise discernable. For example, known methods of mapping the internet typically involve associating websites that link to each other or have common key words. Unlike known methods, embodiments described herein can identify similarities between websites based on actual patterns of user interaction with websites. As with techniques described above for identifying subpopulations of an audience, known techniques for mapping the internet are generally inadequate to identify groups of websites based on actual visitor behavior.

Meaningful distances between websites can be established by defining an embedding. An embedding is a relatively low-dimensional, learned continuous vector representation of a group of relatively high-dimensional vectors. Generating an embedding allows for the reduction of dimensionality while meaningfully representing the high-dimensional vectors in the embedding space.

A website embedding is a mapping from a website to a point in a p-dimensional vector space, where websites containing similar content are mapped to nearby points. At 410, website visitation data can be received from a number of users whose internet activity has been monitored (e.g., by cookie-based tracking or any other suitable technique). Typically, website visitation from over 1,000,000, over 100,000,000, over 200,000,000 or over 500,000,000 users would be received. The website visitation data for each user may include a list of all websites visited by that user and the order in which the websites were visited. At 420, a machine learning technique and/or neural network can be applied to the visitation data received at 410 and define associations between websites based on which sites are frequently viewed in sequence

At 430, an embedding of the websites in p-dimensional space can be defined based on the associations between the websites, creating a p-dimensional map of the internet. 128 is an example of a suitable p, and, for ease of description, references herein to 128 should be understood as referring to p. It should be understood, however, that p can be any suitable integer greater than 2, such as 3, 4, 10, 50, 100, 200, or 500. FIG. 6 is a visualization of websites modeled into a 128-dimensional space and then projected into a 2-d space.

For example if multiple users are observed visiting www.netflix.com and www.hbo.com within a predetermined period of time and/or within a predetermined sequence (e.g., within 20 minutes, within an hour, without visiting any intervening websites, with fewer than five intervening websites, etc.), and similarly, multiple users (not necessarily the same users) are observed visiting www.tvtropes.com and www.hbo.com, then www.tvtropes.com and www.netflix.com can be mapped closer to each other in the 128-dimensional embedding. Moreover, two websites (target websites) viewed in the same context (where context is the sequence of websites visited before or after the target website) can be moved closer to each other based on the frequency of websites viewed in the same context as observed over the set of all users.

At 440, groups or clusters of websites can be identified. For example, websites located near each other in the 128-dimensional space (according to any suitable distance metric) can be identified as belonging to a cluster, using k-means or another suitable clustering technique. A cluster of websites may define an audience (users who have visited a minimum number of websites within the cluster). This audience may contain users that would be receptive to certain targeted content who would not otherwise have been identified, for it may not otherwise have been known that users who visit www.tvtropes.com are good candidates for advertisements about Netflix. Content and/or targeted advertising can be delivered to that audience.

Clusters of websites can be characterized and/or users can be associated with one or more clusters of websites, at 450. For example, a cluster of websites can be characterized by analyzing the website visitation data of users who visit websites within that cluster (e.g., users whose website visitation data indicates a minimum number of visits to websites in that cluster). Features of users who visit a particular cluster can be used to describe or classify that cluster. For example, if website visitation data of visitors to websites within a cluster characteristically overindexes a particular website (a particular website appears more frequently than it does in website visitation data of a random sample of users), that overindexing website can be used to characterize the cluster. Typically, the overindexing website will be within the cluster, but in some instances, a cluster can be characterized by an overindexing website that is not within the cluster or an overindexing cluster other than that cluster.

The website clusters may also be used to describe users or groups of users. For example, if one user has visited only www.tvtropes.com and another has visited only www.netflix.com, without clustering such websites together, it would not be obvious that those users share an actionable similar website visitation history. Grouping the websites into clusters provides a way to capture multiple actions under the same label and provides more descriptive power for understanding audiences and/or selecting audiences to receive targeted content.

In some embodiments, a position of users in the 128-dimensional space can be determined. For example, each website visited by a user can be (e.g., as ascertained by website visitation data associated by that user) can have a position in the 128-dimensional space. Each user's position can be an average, mean, median, max, or other suitable representative metric or summary of that user's web visitation history. In some instances, a user's positions in the 128-dimensional space (e.g., a vector having a length of 128) can be used as dense behavioral vectors and used to select and/or facilitate the delivery of suitable targeted content, cluster users, as described above at 245, 250, and/or 255, or otherwise analyze user behavior and/or make predictions about users' affinities. Similarly stated, determining a position of a user in the 128-dimensional space can be an alternative densification technique to the application of supervised models, as described above at 230 and/or 240. In other instances, a sparse behavioral vector can be densified based on the position of websites visited by an associated user in 128-dimensional without directly determining a position of that user in the 128-dimensional embedding. For example, a dense behavioral vector having a length of 256 can be defined by concatenating a mean position of websites visited by that user (a vector having a length of 128) and a max position of websites visited by that user (a vector having a length of 128).

The user's website visitation history within the 128-dimensional space can provide insights into the user's affinity for particular websites/website clusters even if that user has not been observed visiting those particular websites. For example, a user who has visited a website within a predetermined distance of a target website within the 128-dimensional map of the internet can be identified as an audience member or likely audience member of that target website, even if the user has not been observed visiting that target website. Content and/or advertisements for the user can be selected based on identifying the user as an audience member or likely audience member of a website.

The method described with reference to FIGS. 7 and 8 is particularly well suited to determine clusters of websites. It may be possible to use a technique similar to the method shown and described with reference to FIGS. 7 and 8 to clustering users. Because a user typically visits hundreds to thousands of websites, while each website may be visited by thousands to millions of users, however, the method of FIG. 7 will often provide a more meaningful embedding for websites than users. Similarly stated an embedding of websites defined by the method of FIG. 7 is likely to produce more actionable and/or measurable associations between websites than an embedding of users would produce actionable and/or measurable associations between users.

This mapping of websites can be used to inform the unsupervised clustering described in FIG. 2 . In one embodiment, this mapping of websites can be used to select m targets for the m supervised models described in 230. Similarly stated, at 230, each of the m supervised models can be configured to predict a user's affinity for, likelihood of visiting, or other behavior associated with a cluster identified at 440. For example, k could be selected to be greater to be equal to the desired m, and one website from each of the k clusters could be selected, in order to guarantee that the m target URLs represent a variety of web behaviors.

In addition or alternatively, the 128-dimensional space mapping websites can be used as a dense, lower dimensional space to describe users, in order to define a distance metric between users that can be used to perform the unsupervised user clustering described above (e.g., at 250 as discussed with reference to FIG. 2 ) to product audience subpopulations. Users can be mapped into the 128-dimensional space based on their website visitation data. For example, an average location along each of the 128 dimensions can be calculated for a user based on that user's website visitation data, or a combination of the mean, median, minimum, maximum, and/or other properties along each of the 128 dimensions can be treated as a dense vector to describe the user.

FIG. 9 is a flow chart of a method for generating a conversion action likelihood, according to an embodiment. At 910, website visitation data for a first group of user devices can be received and/or accessed. At 920, the website visitation data for the first group of user devices can be used to generate a 128-dimensional embedding of websites in a manner similar to that described above with reference to FIGS. 7 and 8 , particularly events 410, 420, and 430.

At 930, data, including website visitation data and/or conversion event data associated with a second group of user devices can be accessed and/or received. The second group of user devices can be the same as, partially overlap with, or be mutually exclusive from the first group of user devices. The conversion event data can indicate that a subset of the second group of user device engaged in a conversion action such as, for example, purchasing a good or service, clicking an advertisement, visiting a particular website (e.g., associated with a brand), visiting a physical retail location, etc.

At 940, a position of each website indicated in the website visitation data associated with the second group of user devices can be determined. The positions of websites indicated in the website visitation data can be associated with user devices. Thus, as discussed above, positions of user devices from the second group of user devices in the 128-dimensional space can be determined based on a characteristic or summary metric (e.g., mean, median, max etc.) of the positions of websites associated with that user.

Using the conversion event data and the website visitation records for the second group of user devices, a machine learning model can be trained to predict the likelihood of a conversion event, such as the purchase a good or service, occurring given coordinates in the 128-dimensional embedding, at 950. For training purposes, the position in the 128-dimensional space of user devices in the second group of user devices and/or websites indicated in the user device's website visitation records is used to predict likelihood of a conversion event. Similarly stated, the coordinates in the 128-dimensional space of websites visited and/or user devices associated with conversion actions can tend to indicate that that portion of the embedding is associated with conversions, while the coordinates in the 128-dimensional space of websites visited and/or user devices that are not associated with conversion actions can be negatively correlated with conversions. Thus, the trained model can be operable to return a likelihood of a conversion event occurring, given a coordinates in the 128-dimensional space.

At 960, an indication that a user device is accessing a website is received. The user device may not be from the first group of user devices, the second group of user devices, and/or be a device for which historic website visitation is available. In some instances the user device can be “untrackable.” For example, the device may have deleted or disabled cookies, may present a generic or spoofed user agent, may access the website via a virtual private network, and/or otherwise have taken steps to obfuscate its web browsing history. Using known methods, significant challenges exist selecting targeted content for untrackable devices. Additionally, the website may not have appeared in the website visitation data for any of the second group of user devices. Thus, there may not be any direct data linking conversion event data to the website.

At 970, a position of the website accessed by the user device in the 128-dimensional space can be identified. A relatively small amount of data related to the website (e.g., data from a small subset of the first group of user devices observed visiting the website, which may not include brand or purchase-related data) can be used to determine the location of the website in the 128-dimensional space, which can in turn be used as a privacy-sensitive proxy to select appropriate targeted content to be served with the website. Similarly stated, the position of the website can be provided to the trained model, which can produce a value indicative of a likelihood of a conversion event occurring (e.g., the likelihood of a purchase of a good and/or service), at 980. Targeted content can be delivered to the user device based on the output of the trained model, for example, embedded in the webpage accessed by the user device.

The machine learning model trained at 950 can be privacy-sensitive, such that an advertisement display opportunity is not required to include privacy-sensitive contents as input to the machine learning model. For example, the advertisement display opportunity triggered by the user device's access to the webpage at 960 may not include or be associable with website traffic data records of the user devices. Additionally, the machine learning model trained at 950 may not require, as an input, any information associated with the textual or non-textual contents of the webpage.

While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. Furthermore, although various embodiments have been described as having particular features and/or combinations of components, other embodiments are possible having a combination of any features and/or components from any of embodiments where appropriate as well as additional features and/or components. For example, although FIG. 1 depicts a single audience identification device 110 and a single targeted content provider 120, it should be understood that this is for ease of description and illustration only. In other embodiments, a system can include any number of audience identification device and/or targeted content providers. Moreover, it should be understood that computing entities, processors, and/or memories described herein can include distributed architectures. Furthermore, although shown as separate, in some embodiments, various computing entities can be physically and/or logically collocated. For example, the audience identification device 110 and the targeted content provider 120 can be a single logical and/or physical device.

Some embodiments described herein relate to methods. It should be understood that such methods may be computer-implemented methods (e.g., instructions stored in memory and executed on processors). Where methods described above indicate certain events occurring in certain order, the ordering of certain events may be modified. Additionally, certain of the events may be performed repeatedly, concurrently in a parallel process when possible, as well as performed sequentially as described above. Furthermore, certain embodiments may omit one or more described events.

Some embodiments described herein relate to computer-readable medium. A computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not include transitory propagating signals per se (e.g., a propagating electromagnetic wave carrying information on a transmission medium such as space or a cable). The media and computer code (also can be referred to as code) may be those designed and constructed for the specific purpose or purposes. Examples of non-transitory computer-readable media include, but are not limited to: magnetic storage media such as hard disks, floppy disks, and magnetic tape; optical storage media such as Compact Disc/Digital Video Discs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), and holographic devices; magneto-optical storage media such as optical disks; carrier wave signal processing modules; and hardware devices that are specially configured to store and execute program code, such as ASICs, PLDs, ROM and RAM devices. Other embodiments described herein relate to a computer program product, which can include, for example, the instructions and/or computer code discussed herein.

Examples of computer code include, but are not limited to, micro-code or micro-instructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higher-level instructions that are executed by a computer using an interpreter. For example, embodiments may be implemented using Java, C++, or other programming languages (e.g., object-oriented programming languages) and development tools. Additional examples of computer code include, but are not limited to, control signals, encrypted code, and compressed code. 

What is claimed is:
 1. A computer-implemented method, comprising: accessing behavioral data associated with a plurality of users; defining a plurality of sparse behavioral vectors based on the behavioral data, each sparse behavioral vector from the plurality of sparse behavioral vectors associated with a user from the plurality of users, each element of each sparse behavioral vector from the plurality of sparse behavioral vectors representing a different potential detectable behavior from a set of potential detectable behaviors such that the behavioral data is encoded in the plurality of sparse behavioral vectors; applying a plurality of supervised learning models to each sparse behavioral vector from the plurality of sparse behavioral vectors, each supervised learning model from the plurality of supervised learning models trained to produce a score predictive of whether a user from the plurality of users will perform a different potential detectable behavior from the set of potential detectable behaviors based on a sparse behavioral vector from the plurality of sparse behavioral vectors and associated with that user; define a plurality of dense behavioral vectors, each dense behavioral vector from the plurality of dense behavioral vectors uniquely associated with a sparse behavioral vector from the plurality of sparse behavioral vectors and having a plurality of elements, for each dense behavioral vector from the plurality of dense behavioral vectors, each element derived from a score from a different supervised learning model from the plurality of supervised learning models such that each dense behavioral vector from the plurality of dense behavioral vectors represents a plurality of independent predictive scores associated with different potential detectable behaviors from the set of potential detectable behaviors; and applying an unsupervised machine learning technique to the plurality of dense behavioral vectors to define a plurality of subpopulations based on similarities between dense behavioral vectors from the plurality of dense behavioral vectors.
 2. The computer-implemented method of claim 1, wherein at least a subset of the set of potential detectable behaviors are visits to websites.
 3. The computer-implemented method of claim 1, wherein a median sparse behavioral vector from the plurality of sparse behavioral vectors is at least 99.9% sparse.
 4. The computer-implemented method of claim 1, wherein the plurality of supervised learning models includes at least 500 supervised learning models.
 5. The computer-implemented method of claim 1, further comprising: applying a machine learning technique to the behavioral data to define associations between a plurality of websites represented in the behavioral data; defining an embedding of the plurality of websites in p-dimensional space based on the associations between the plurality of websites; identifying a plurality of clusters of websites based on proximity of websites from the plurality of websites to each other in the p-dimensional space identifying a plurality of target websites, each target website from the plurality of target websites characteristic of a cluster from the plurality of clusters; training the plurality of supervised learning models, each supervised learning model from the plurality of supervised learning models trained to predict a likelihood of a visit to a target website from the plurality of target websites.
 6. The computer-implemented method of claim 1, further comprising facilitating delivery of targeted content to a user from the plurality of users based on a dense behavioral vector (1) from the plurality of dense behavioral vectors and (2) associated with that user being associated with a subpopulation from the plurality of subpopulations.
 7. The computer-implemented method of claim 6, wherein: the behavioral data is associated with a first time period; the plurality of users is a first plurality of users; the plurality of sparse behavioral vectors is a first plurality of sparse behavioral vectors associated with the first time period; the plurality of dense behavioral vectors is a first plurality of dense behavioral vectors associated with the first time period; and the plurality of subpopulations is a first plurality of subpopulations associated with the first time period, the method further comprising: accessing behavioral data associated with a second period of time and a second plurality of users, the first plurality of users and the second plurality of users having at least one common user; defining a second plurality of sparse behavioral vectors associated with the second plurality of users; densifying the second plurality of sparse behavioral vectors by applying the plurality of supervised learning models to each sparse behavioral vector from the second plurality of sparse behavioral vectors to define a second plurality of dense behavioral vectors; applying the unsupervised machine learning technique to the second plurality of dense behavioral vectors to define a second plurality of subpopulations based on similarities between dense behavioral vectors from the second plurality of dense behavioral vectors; compare a subpopulation from the second plurality of subpopulations to each subpopulation from the first plurality of subpopulations; determine that the subpopulation is a new subpopulation based on no corresponding subpopulation being identified in the first plurality of subpopulations; and facilitate the modification of the targeted content based on emergence of the new subpopulation.
 8. The computer-implemented method of claim 7, wherein the first time period and the second time period are mutually exclusive.
 9. The computer-implemented method of claim 7, wherein there is no predefined concordance between any subpopulation from the first plurality of subpopulations and any subpopulation from the second plurality of subpopulations.
 10. A non-transitory processor-readable medium storing code configured to be executed by a processor, the code including instructions configured to cause the processor to: access a plurality of sparse behavioral vectors, each sparse behavioral vector from the plurality of sparse behavioral vectors associated with a user from a plurality of users, each element of each sparse behavioral vector representing a different potential detectable behavior; apply a plurality of supervised learning models to the plurality of sparse behavioral vectors, each supervised learning model from the plurality of supervised learning models uniquely associated with a potential detectable behavior and configured to produce a score representing a probability that a user will perform that potential detectable behavior; define a plurality of dense behavioral vectors, each dense behavioral vector from the plurality of dense behavioral vectors uniquely associated with a sparse behavioral vector from the plurality of sparse behavioral vectors and having a plurality of elements, each element from the plurality of elements of each dense behavioral vector derived from a score produced by a different supervised learning model from the plurality of supervised learning models such that each dense behavioral vector from the plurality of dense behavioral vectors represents a plurality of independent predictive scores associated with different potential detectable behaviors; apply an unsupervised machine learning technique to the plurality of dense behavioral vectors to define a plurality of subpopulations based on similarities between dense behavioral vectors from the plurality of dense behavioral vectors; and facilitate delivery of targeted content to a user from the plurality of users based on a dense behavioral vector (1) from the plurality of dense behavioral vectors and (2) associated with that user being associated with a subpopulation from the plurality of subpopulations.
 11. The non-transitory processor-readable medium of claim 10, wherein each supervised learning model from the plurality of supervised learning models is trained independently from the plurality of sparse behavioral vectors.
 12. The non-transitory processor-readable medium of claim 10, wherein at least one supervised machine learning model from the plurality of supervised learning models is associated with a potential detectable behavior that no sparse behavioral vector from the plurality of sparse behavioral vectors indicates as being detected.
 13. The non-transitory processor-readable medium of claim 10, wherein: each element of each sparse behavioral vector has a binary value; and each element of each dense behavioral vector has a floating point value.
 14. The non-transitory processor-readable medium of claim 10, wherein each supervised learning model from the plurality of supervised learning models is configured to predict a likelihood that each user from the plurality of users will visit a different website, based on a sparse behavioral vector from the plurality of sparse behavioral vectors that is associated with that user.
 15. The non-transitory processor-readable medium of claim 10, the code further comprising code to cause the processor to: receive website visitation data associated with each user from the plurality of users; and define the plurality of sparse behavioral vectors based on the website visitation data.
 16. The non-transitory processor-readable medium of claim 10, the code further comprising code to cause the processor to: place each dense behavioral vector from the plurality of dense behavioral vectors in n-dimensional space based on distances between the plurality of dense behavioral vectors, the unsupervised machine learning technique applied to the plurality of dense behavioral vectors as they exist in the n-dimensional space to define the plurality of subpopulations. 